The Growth and Diversity of Older Undocumented Immigrants in the United States
Bibliographic record
Abstract
The undocumented immigrant population in the United States is aging and diversifying by origin group. However, research on aging among undocumented immigrants focuses on Mexicans and Central Americans, even as this population declines, and less is known about other groups. We analyze residual estimates of the undocumented population and the 2018‒2022 panels of the Survey of Income and Program Participation to document trends in age at arrival, duration in undocumented status, and socioeconomic and health correlates for undocumented immigrants across 27 countries or regions. We find dramatic increases in the older undocumented population across all origin groups, especially among those from Asia, the Caribbean, Europe, Canada, and Oceania. Aging in place drives population aging among the largest groups-those from Mexico, Central America, Venezuela, and India-while both aging in place and increases in arrivals at older ages are responsible for population aging among those from other origins. Additionally, undocumented status for older immigrants from most origins is associated with significant socioeconomic disadvantage regardless of age at arrival, but especially for those who age in place. This finding foreshadows rising inequality by legal status among America's seniors as the most disadvantaged immigrant groups age in place in coming decades.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".